Energy optimization operation method and system of rural light-moore virtual power plant considering energy storage degradation

Abstract

The application discloses a rural light-moat virtual power plant energy optimization operation method and system considering energy storage degradation, comprising: establishing an energy storage degradation cost model and basic operation constraints of a virtual power plant; a virtual power plant upper model predictive control layer considers dynamic energy storage degradation cost, power purchase and sale cost and total operation and maintenance cost to perform virtual power plant energy operation optimization, and obtains upper optimization scheduling instructions; the upper optimization scheduling instructions are input to a lower model predictive controller, the deviation cost of the upper reference value and the energy storage scheduling cost are comprehensively considered to perform source and load power fluctuation suppression, and lower operation results are obtained; the lower operation results are fed back to the upper model predictive control layer to perform virtual power plant energy circulation iterative operation optimization. The application flexibly adjusts rural photovoltaic and biogas resources, is suitable for rural power distribution networks with a large number of renewable energy access, reduces the average degradation cost of energy storage batteries and the average operation cost of the system on the premise of efficient new energy consumption.

Description

Technical Field

[0001] The present invention belongs to the technical field of comprehensive energy utilization, and relates to an energy optimization operation method and system for a rural solar-biogas virtual power plant taking into account energy storage degradation. Background Art

[0002] With the development of renewable energy in my country, the proportion of photovoltaic and biogas power generation connected to rural areas has increased year by year. However, the integration of these new energy sources has left rural power grids with poor source-load balancing capabilities and a lack of effective regulation methods to maximize the economic benefits of new energy integration. At the same time, the integration of new energy sources has increased the complexity and uncertainty of rural power grids and introduced a large number of flexible resources.

[0003] Virtual power plants, as a regulatory tool that effectively aggregates flexible resources, are an important means of achieving efficient utilization of rural solar-biogas resources and improving the operational safety and economic efficiency of rural power grids. However, existing research on rural virtual power plants typically only considers the economic benefits of real-time energy management systems operating under varying resource, load, and environmental conditions, while ignoring or assuming only fixed prices as operating costs. Unlike power generation resources, the frequent short-term scheduling of virtual power plants can have a significant impact on the long-term lifespan of the entire system. For example, frequent charging and discharging can significantly shorten the battery life of the energy storage component of a virtual power plant. Furthermore, the conflict between economic efficiency and stability further complicates energy management in virtual power plants. Therefore, it is important to study energy optimization scheduling methods for rural solar-biogas virtual power plants that consider energy storage degradation. Summary of the Invention

[0004] To address the deficiencies in the prior art, the present invention provides an energy optimization operation method and system for a rural photovoltaic-biogas virtual power plant that takes energy storage degradation into consideration. This method can flexibly adjust rural photovoltaic and biogas resources and is suitable for rural distribution networks with a large amount of renewable energy access. While efficiently absorbing new energy, it can reduce the average degradation cost of energy storage batteries and the average operating cost of the system.

[0005] The present invention adopts the following technical solutions.

[0006] A first aspect of the present invention provides an energy optimization operation method for a rural solar-biogas virtual power plant taking into account energy storage degradation, comprising the following steps:

[0007] Establish energy storage degradation cost models and basic operating constraints for virtual power plants;

[0008] The upper-level model predictive control layer of the virtual power plant, based on the energy storage degradation cost model, considers dynamic energy storage degradation costs, electricity purchase and sales costs, and total operation and maintenance costs, optimizes the energy operation of the virtual power plant based on the basic operating constraints of the virtual power plant, and obtains upper-level optimization scheduling instructions;

[0009] The upper-level optimization scheduling instructions are input into the lower-level model predictive controller. The lower-level model predictive controller comprehensively considers the deviation cost of the upper-level reference value and the energy storage scheduling cost, and smoothes the source-load power fluctuation based on the basic operating constraints of the virtual power plant to obtain the lower-level operation results. The lower-level operation results are fed back to the updated upper-level model predictive control layer to perform iterative operation optimization of the virtual power plant energy cycle.

[0010] Preferably, the energy storage degradation cost model is as follows:

[0011]

[0012] Where C BDC (t,d B (Δt)) is the degradation cost of the energy storage battery at time t;

[0013] Δt is the time interval between discharge events;

[0014] C B The replacement cost of energy storage batteries;

[0015] P B (t) is the power of the energy storage battery at time t within Δt;

[0016] L B (d B (Δt)) is the depth of charge and discharge d B Energy storage battery life at (Δt);

[0017] d B (Δt) is the charge and discharge depth of the energy storage battery within Δt;

[0018] E BA (t) is the actual capacity of the energy storage battery at time t;

[0019] η Bc and η Bd are the charging and discharging efficiency coefficients of the energy storage battery, respectively.

[0020] Preferably, the basic operating constraints of the virtual power plant include the photovoltaic output constraints of the virtual power plant, the biogas gas turbine output constraints, the energy storage battery charging and discharging operation constraints, the energy storage battery state of charge constraints, the power capacity constraints of the power grid, energy storage and biogas, and the power balance constraints.

[0021] Preferably, the photovoltaic output constraint of the virtual power plant is:

[0022] 0≤P PV (t)≤P PVmax (2)

[0023] Where, P PV(t) is the power of the photovoltaic power generation system of the virtual power plant at time t;

[0024] P PVmax is the maximum power of the photovoltaic power generation system of the virtual power plant;

[0025] The biogas gas turbine output constraint is:

[0026] 0≤P Bi (t)≤P Bimax (3)

[0027] Where, P Bi (t) represents the power of the biogas gas turbine of the virtual power plant at time t;

[0028] P Bimax is the maximum power of the biogas gas turbine of the virtual power plant;

[0029] The charge and discharge operation constraints of the energy storage battery are:

[0030]

[0031] Where, P Chr (t) is the charging power of the energy storage battery of the virtual power plant at time t;

[0032] P Disc (t) is the discharge power of the energy storage battery of the virtual power plant at time t;

[0033] is the maximum charge and discharge power of the energy storage battery;

[0034] δ BS The charging and discharging efficiency of the energy storage battery;

[0035] E BS (t) is the electromotive force of the energy storage battery;

[0036] are the highest and lowest electromotive force of the energy storage battery respectively;

[0037] The energy storage battery state of charge constraint is:

[0038]

[0039] Where, are the maximum and minimum charge factors of the energy storage battery respectively;

[0040] t u ,t l are the time points of the upper and lower time domains, respectively;

[0041] The power capacity constraints of the grid, energy storage and biogas are:

[0042]

[0043] Where, P M (t), P B (t) grid exchange and energy storage battery discharge respectively;

[0044] The power balance constraint is:

[0045] P L (t) = P M (t)+P B (t)+P PV (t)+P Bi (t), t∈{t1,t u} (10)

[0046] Where, P L (t), P M (t), P PV (t), P B (t), P Bi (t) are load, grid exchange, photovoltaic, energy storage battery discharge, and biogas power generation power, respectively.

[0047] Preferably, the objective function of the upper model predictive control layer for optimizing the energy operation of the virtual power plant is:

[0048]

[0049] Where, F u Represents the objective function of the upper-layer model of the virtual power plant for optimizing the energy operation of the virtual power plant;

[0050] Indicates t u The cost of electricity purchase and sale by the virtual power plant at the time;

[0051] Indicates t u Total operation and maintenance costs of photovoltaic, biogas and energy storage at any given moment;

[0052] Indicates t u Dynamic energy storage degradation cost at each moment;

[0053] T u Indicates the prediction length of the upper model.

[0054] Preferably, The calculation formulas are:

[0055]

[0056] Where C buy (t u ) and Pbuy (t u ) are t u The electricity purchase price and power at the time;

[0057] C sell (t u ) and P sell (t u ) are t u The electricity selling price and power at the time;

[0058] r PV 、r Bi 、r B are the operation and maintenance cost coefficients of photovoltaic, biogas, and energy storage batteries respectively;

[0059] P PV (t u ) is t u The power of the photovoltaic power generation system of the virtual power plant at each moment;

[0060] P Bi (t u ) represents t u The power of the biogas gas turbine in the virtual power plant at any moment;

[0061] P B (t u ) represents t u The power of the energy storage battery of the virtual power plant at any moment;

[0062] g(t u ) is the state transition variable of the charge and discharge events in two consecutive time intervals;

[0063] t u , t u -1 moment of energy storage battery degradation cost;

[0064] E B (t u ), E B (t u -1) are t u , t u -1 moment the amount of power stored in the energy storage battery is reduced.

[0065] Preferably,

[0066] Where, P B (t u -1) indicates t u -1 The power of the virtual power plant energy storage battery at time.

[0067] Preferably, the objective function of the lower-layer model predictive controller for smoothing source-load power fluctuations is:

[0068]

[0069] Where, F l Predict the objective function of the controller for the underlying model;

[0070] is the deviation cost of the upper reference value;

[0071] The cost of energy storage dispatch;

[0072] T l Indicates the prediction length of the underlying model.

[0073] Preferably, The calculation formulas are:

[0074]

[0075] Where, are the prediction deviations of biogas, energy storage, and grid exchange power, respectively;

[0076] is the prediction deviation penalty coefficient for biogas, energy storage, and grid exchange power;

[0077] is the energy storage dispatch cost coefficient;

[0078] Provide energy storage after dispatch;

[0079] To provide energy storage before dispatch;

[0080] Δt l is the time interval of the lower model.

[0081] Preferably, The calculation formula is:

[0082]

[0083] Where, P Bi (t l ), P B (t l ), P M (t l ) are t l The output of biogas, energy storage and power grid at all times;

[0084] are t obtained by upper layer optimization respectively. l Reference output values ​​of biogas, energy storage and power grid at all times.

[0085] A second aspect of the present invention provides an energy optimization operation system for a rural solar-biogas virtual power plant taking into account energy storage degradation, comprising:

[0086] Cost model and constraint building module, used to establish energy storage degradation cost model and basic operating constraints of virtual power plants;

[0087] The upper-level model predictive control module is used for the upper-level model predictive control layer of the virtual power plant. Based on the energy storage degradation cost model, the upper-level model predictive control layer considers the dynamic energy storage degradation cost, the purchase and sale cost of electricity, and the total operation and maintenance cost. It optimizes the energy operation of the virtual power plant based on the basic operation constraints of the virtual power plant and obtains the upper-level optimization scheduling instructions.

[0088] The lower-level model predictive control module is used to input the upper-level optimization scheduling instructions into the lower-level model predictive controller. The lower-level model predictive controller comprehensively considers the deviation cost of the upper-level reference value and the energy storage scheduling cost, and smoothes the source and load power fluctuations based on the basic operating constraints of the virtual power plant, obtains the lower-level operation results, and feeds back the lower-level operation results to the updated upper-level model predictive control layer to perform iterative operation optimization of the virtual power plant energy cycle.

[0089] A terminal includes a processor and a storage medium; the storage medium is used to store instructions;

[0090] The processor is configured to operate according to the instructions to execute the steps of the method.

[0091] A computer-readable storage medium stores a computer program, which implements the steps of the method when executed by a processor.

[0092] Compared with the prior art, the beneficial effects of the present invention include at least:

[0093] (1) The present invention takes energy storage degradation into consideration and improves the energy supply flexibility of rural solar virtual power plants under different operating conditions by aggregating flexible control resources in rural areas. It also reduces the average degradation cost of energy storage batteries and the average operating cost of the system while ensuring that rural microgrids can safely and efficiently absorb new energy. This improves the new energy absorption capacity of rural areas and can effectively improve the economic benefits of production and life in rural areas.

[0094] (2) The upper-level model objective function takes into account the energy storage degradation cost on the basis of the basic electricity purchase and sales cost and the system operation and maintenance cost, and realizes the coordinated optimization of multiple cost factors, so that the virtual power plant can better control cost expenditure during operation, extend the battery life, and improve the long-term economic benefits of the virtual power plant.

[0095] (3) The dynamic energy storage degradation cost calculation model of the present invention clarifies the calculation method of the energy storage battery degradation cost in continuous time, dynamically estimates the battery degradation degree and corresponding cost, and improves the accuracy of battery degradation cost prediction.

[0096] (4) The objective function of the lower-level model comprehensively considers the prediction error of the upper-level model (the deviation cost of the upper-level reference value) and the energy storage scheduling cost, so that the sum of the impact cost of the prediction error fluctuation and load power change on the system and the energy storage response cost to cope with power fluctuation is minimized, balancing the scheduling efficiency and the cost of energy storage, making the system more robust while minimizing the scheduling cost. BRIEF DESCRIPTION OF THE DRAWINGS

[0097] Figure 1 This is a flow chart of an energy optimization operation method of a rural solar-biogas virtual power plant considering energy storage degradation according to the present invention;

[0098] Figure 2 It is a hierarchical structure for energy optimization operation of the rural solar-biogas virtual power plant of the present invention;

[0099] Figure 3 This is a flow chart of the energy optimization operation of the rural solar-biogas virtual power plant based on the hierarchical structure of the present invention. DETAILED DESCRIPTION

[0100] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. The embodiments described in this application are only part of the embodiments of the present invention, not all of them. Based on the spirit of the present invention, other embodiments obtained by ordinary technicians in this field without making creative efforts are all within the scope of protection of the present invention.

[0101] Embodiment 1 of the present invention provides an energy optimization operation method for a rural solar-biogas virtual power plant taking into account energy storage degradation, such as Figure 1 As shown, the following steps are included:

[0102] S1. Establish an energy storage degradation cost model and basic operating constraints for virtual power plants;

[0103] Further preferably, according to the battery energy storage cost degradation caused by the charge and discharge depth and service life, an energy storage degradation cost is established, and the energy storage degradation cost is a degradation conversion model that takes into account the battery charge and discharge depth and battery life;

[0104] The energy storage degradation cost model established specifically considers a discharge event starting at time t. The corresponding battery degradation cost within a time interval Δt is as follows:

[0105]

[0106] Where, d B (Δt) is the depth of charge and discharge of the battery within the time interval Δt, C BDC (t,d B (Δt)) is the battery degradation cost corresponding to the discharge event, C B is the battery replacement cost, P B (t) is the average power during the discharge event time interval Δt, L B (d B (Δt)) is the depth of charge and discharge d B Battery life at (Δt), E BA (t) is the actual capacity at time t, η Bc and η Bd are the charge and discharge efficiency coefficients, respectively.

[0107] After the event, the actual capacity of the battery is reduced proportionally within the time t+Δt as follows:

[0108]

[0109] Where, E B.rated is the rated capacity of the battery.

[0110] Further preferably, the basic operating constraints of the virtual power plant established include photovoltaic output constraints of the virtual power plant, biogas gas turbine output constraints, energy storage battery charging and discharging operation constraints, energy storage battery state of charge constraints, power capacity constraints of the power grid, energy storage and biogas, and power balance constraints;

[0111] Specifically, based on the basic output and operation constraints, constraint models for the virtual power plant's photovoltaic output, biogas gas turbine output, and energy storage battery charging and discharging are established. These mainly include photovoltaic and biogas output constraints, energy storage operation constraints, and power balance constraints during the operation of the virtual power plant. Their composition will meet the conditions required for the safe and stable operation of the virtual power plant under different operating conditions.

[0112] The output constraints of the PV system of the virtual power plant are as follows:

[0113] 0≤P PV (t)≤P PVmax (3)

[0114] Where, P PV (t) is the power of the photovoltaic power generation system at time t, P PVmax is the maximum power of the photovoltaic power generation system.

[0115] The output constraints of biogas gas turbine are as follows:

[0116] 0≤P Bi (t)≤P Bimax (4)

[0117] Where, P Bi (t) represents the power of the biogas gas turbine at time t; P Bimax is the maximum power of the gas turbine;

[0118] The charging and discharging operation constraints of energy storage batteries are as follows:

[0119]

[0120] Where, P Chr (t) is the charging power, P Disc (t) is the discharge power, is the maximum charge and discharge power, δ BS is the charging and discharging efficiency of the energy storage battery, E BS (t) is the electromotive force of the energy storage battery, are the highest and lowest electromotive force respectively.

[0121] In order to prevent the battery from overcharging and discharging, the battery state of charge constraint is considered, which can be expressed as:

[0122]

[0123] Where, are the maximum and minimum charge factors, respectively.

[0124] t u ,t l are the time points of the upper and lower time domains, respectively.

[0125] The inequality constraints in the model mainly include the power capacity limits of the utility grid, energy storage, and biogas:

[0126]

[0127] Where, P M (t), P B (t) are grid exchange and energy storage battery discharge respectively.

[0128] The virtual power plant model must meet the power balance constraints:

[0129] P L (t) = P M (t)+P B (t)+P PV (t)+P Bi (t), t∈{t1,t u} (11)

[0130] S2: The upper-level model predictive control layer of the virtual power plant optimizes the energy operation of the virtual power plant based on the energy storage degradation cost model, taking into account the dynamic energy storage degradation cost, electricity purchase and sales cost, and total operation and maintenance cost, based on the basic operating constraints of the virtual power plant, and obtains the upper-level optimization scheduling instructions;

[0131] S3. Input the upper-level optimization scheduling instructions into the lower-level model predictive controller. The lower-level model predictive controller comprehensively considers the deviation cost of the upper-level reference value and the energy storage scheduling cost, and smoothes the source-load power fluctuation based on the basic operating constraints of the virtual power plant to obtain the lower-level operation results. The lower-level operation results are fed back to the updated upper-level model predictive control layer to perform iterative operation optimization of the virtual power plant energy cycle.

[0132] During specific implementation, the initial operating state and target of the system are used as the initial state of the upper-level model, and the historical energy consumption data of the virtual power plant corresponding to different production links of rural photovoltaic and biogas systems need to be collected, including historical temperature, equipment operating conditions, photovoltaic and biogas output data, user load data and electricity purchase and sales price data; the collected historical energy consumption of the virtual power plant is preprocessed, and the processed historical data is mainly used to establish the virtual power plant system and train prediction capabilities; temperature and equipment operating conditions mainly affect the power generation capacity of photovoltaic and biogas equipment, as well as the demand for some loads (such as fans and refrigeration equipment), which are included in the virtual power plant system.

[0133] Analyze the energy and load elements of the rural virtual power plant, extract the core energy flow characteristics, and collect different energy data through edge-cloud collaboration, as follows:

[0134] Sensors and equipment deployed at key nodes of the virtual power plant collect data such as temperature, equipment operating conditions, initial solar output data, and initial user load data in rural production links. They can also connect to remote IO stations or other controllers to collect data and obtain electricity purchase and sales price data.

[0135] Then, the controller connects to the switch and router via industrial Ethernet and sends the collected data to the cloud platform. For places where it is inconvenient to deploy a wired communication network, wireless communication can be used.

[0136] The information collection and control equipment of the virtual power plant includes dispatching communication equipment, dispatching automation equipment, relay protection equipment, safety automatic devices, power regulation and voltage regulation equipment, etc.

[0137] Preprocess the collected historical energy consumption of the virtual power plant, including normalization, denoising, and supplementation of missing data. Specifically, the collected historical energy consumption data of the virtual power plant is normalized to obtain historical energy consumption data of different energy consumption links, which is then preprocessed to obtain an initial data set, and relevant parameters are set and initialized.

[0138] The collected historical data (including energy consumption data such as electricity and heat consumed in various aspects of production and life of the rural solar-biogas virtual power plant) are normalized using the weighted FCM clustering method for different types of data, and clustered according to different production conditions (season, temperature, different types of production activities, etc.), providing a basis for energy consumption prediction under different operating conditions of the virtual power plant.

[0139] Furthermore, the Savitazky-Golay algorithm is used to denoise the original data, and its expression is:

[0140]

[0141] Where, X j+1 is the data before the j+1th denoising, X j * is the jth denoised data; C i is the weight factor of the i-th value in the smoothing window, k is the width of the smoothing window, j is the initial value subscript, m is the width of half the smoothing window, and the algorithm window covers 2m+1 adjacent point values.

[0142] Furthermore, the missing data of the original data are supplemented by the neighboring interpolation method. The specific method is to use the data at the time closest to the missing data to estimate the data at the missing time. For example, when the sampling rate is at the hourly level, the data of the previous hour and the next hour are used for missing interpolation. In principle, all the data required for prediction need to be supplemented.

[0143] Further preferably, the upper-layer optimization scheduling instructions are transmitted to the lower-layer model prediction controller, and the demand response instructions are fed back based on the load characteristics of the region to achieve source-load power fluctuation smoothing; the lower-layer operation results are fed back to the upper-layer model prediction layer, so as to perform rolling optimization under flexible adjustment of demand response and achieve the optimal overall operating cost considering the energy storage cost on a long time scale.

[0144] The hierarchical structure and process of the two-tier energy management method are as follows: Figure 2 、 Figure 3 As shown, where T u and T1 denote the prediction lengths in the upper and lower layers, respectively.

[0145] The upper layer consists of l ∈{1,…,T u} is composed of a nonlinear rolling model predictive controller, the lower layer consists of a u ∈{1,…,T1} is composed of a quadratic model predictive controller.

[0146] Δt u and Δt lThey represent the time intervals of the upper and lower layers respectively. By solving the objective function of each layer, the control behavior within each time interval is obtained, so that the decision of one layer affects the other layer.

[0147] Based on the given forecast data and the load response concentration of the region, in the upper layer T u The optimal schedule is developed, but only at time Δt u The scheduling within will be used as a reference value to control the scheduling behavior of the lower layer;

[0148] The lower layer optimizes itself through energy storage and minimizes system power fluctuations while responding to regional flexible demand. u After that, the lower layer sends the updated state variables back to the upper layer and starts the next Δt u Scheduling problem.

[0149] Further preferably, the upper model predictive control layer of the virtual power plant considers the cost of electricity purchase and sale, equipment operation and maintenance cost, and energy storage degradation cost, and optimizes the energy operation of the virtual power plant based on the basic operation constraints of the virtual power plant (i.e., the constraints of the objective function using formulas (3)-(11)), and obtains the upper optimization scheduling instructions

[0150] is the optimized P Bi (t u ), P B (t u ), P M (t u ), in order to make the objective function F u middle The smallest group P Bi (t u ), P B (t u ), P M (t u ).

[0151] The objective function of the upper model predictive control layer for optimizing the energy operation of the virtual power plant is:

[0152]

[0153] Where, F u represents the objective function of the upper-level energy optimization operation of the virtual power plant; represents the cost of purchasing and selling electricity for the virtual power plant; represents the operation and maintenance costs of photovoltaic, biogas, and energy storage; represents the energy storage degradation cost.

[0154] The cost of purchasing and selling electricity for a virtual power plant is:

[0155]

[0156] Where C buy (t u ) and P buy (t u ) are t u The electricity purchase price and power at the time, C sell (t u ) and P sell (t u ) are t u The electricity price and power sold at the time, P M is the total exchange power of the grid.

[0157] In formula (14), P buy P sell and P M The relationship is: P M (t u )=P buy (t u )-P sell (t u ), P M is the total exchange power of the grid.

[0158] The operation and maintenance costs of photovoltaic, biogas and energy storage are:

[0159]

[0160] Where r PV 、r Bi 、r B are the operation and maintenance cost coefficients of photovoltaic, biogas and energy storage devices respectively.

[0161] Considering each time interval Δt u Battery degradation costs in It can only be determined after the charge or discharge event is completed, so it is necessary to first determine the battery P B (t u ) power flow direction. Introduced as an auxiliary binary variable g(t u ) to represent the state transition of charging and discharging events between two consecutive time intervals.

[0162]

[0163] Battery degradation cost over consecutive time intervals Through g(t u ) to express:

[0164]

[0165] in, t u , t u -1 moment energy storage degradation cost;

[0166] E B (t u ), E B (t u -1) are t u , t u -1 moment the amount of power stored in the energy storage battery is reduced.

[0167] Calculate using formula (1): Substitute formula (1) C BDC (t,d B t and d in (Δt) B (Δt) is replaced by Perform calculations, Indicates the depth of charge and discharge d B (Δt).

[0168] E B (t u ), E B (t u -1) is calculated using formula (2), that is, after the event, the actual capacity of the energy storage battery is proportionally reduced within the time t+Δt as follows: Where, E B.rated is the rated capacity of the energy storage battery.

[0169] The lower-level model predictive controller considers the penalty cost caused by the deviation from the upper-level reference value and the energy storage dispatch cost of smoothing power fluctuations, inputs the upper-level optimization dispatch instruction into the lower-level model predictive controller, and smoothes the source-load power fluctuation based on the basic operation constraints of the virtual power plant of S4 (i.e., the constraints of formulas (3)-(11) as the objective function). The energy storage response minimizes the impact of the prediction error fluctuation and load power change on the system (by increasing or decreasing the output of the energy storage battery to compensate for the deviation of the upper-level model prediction, so as to smooth the power fluctuation), and obtains the lower-level operation result. The lower-level operation results are returned to update the upper-level model prediction control layer to optimize the energy operation of the virtual power plant.

[0170] P Bi (t l ), P B (t l ), P M (t l ) is the optimal value, even if the objective function F l middle The smallest group PBi (t l ), P B (t l ), P M (t l );

[0171] Lower-level operation results Input to the upper model of the virtual power plant as its objective function and P in the constraints Bi (t u ), P B (t u ), P M (t u ).

[0172] The objective function of the lower-level model predictive controller for smoothing source-load power fluctuations is:

[0173]

[0174] Where, is the deviation cost of the upper reference value, is the energy storage dispatch cost.

[0175] The deviation cost of the upper reference value is:

[0176]

[0177] Where, are the prediction deviations of biogas, energy storage, and grid exchange power, respectively. is the prediction deviation penalty coefficient.

[0178]

[0179] Where, P Bi (t l ), P B (t l ), P M (t l ) are biogas, energy storage, and power grid at t l The effort of every moment, are t obtained by upper layer optimization respectively. l Reference output values ​​of biogas, energy storage and power grid at all times.

[0180] The energy storage dispatch cost is:

[0181]

[0182] Where, is the energy storage dispatch cost coefficient, For the energy storage output after scheduling, To provide energy storage before dispatching.

[0183] Feeding back the lower-level operational results to the upper-level model prediction layer allows for rolling optimization with flexible demand response, achieving optimal overall operating costs over long timescales, taking into account energy storage costs. By executing these steps, the virtual power plant can continuously optimize its energy usage structure and respond promptly to load demand, thereby increasing the proportion of renewable energy consumption in rural areas.

[0184] Overall, the present invention can provide a reliable reference for the operation of rural virtual power plants, effectively reduce the economic costs caused by energy storage degradation based on the energy storage characteristics, and improve the flexibility and accuracy of energy supply design, thereby avoiding energy waste to the greatest extent; on this basis, different types of demand responses of rural solar-biogas virtual power plants are achieved through double-layer energy optimization, further forming an energy optimization operation method for rural solar-biogas virtual power plants that takes into account energy storage degradation.

[0185] Embodiment 2 of the present invention provides an energy optimization operation system for a rural solar-biogas virtual power plant taking into account energy storage degradation, including:

[0186] Cost model and constraint building module, used to establish energy storage degradation cost model and basic operating constraints of virtual power plants;

[0187] The upper-level model predictive control module is used for the upper-level model predictive control layer of the virtual power plant. Based on the energy storage degradation cost model, the upper-level model predictive control layer considers the dynamic energy storage degradation cost, the purchase and sale cost of electricity, and the total operation and maintenance cost. It optimizes the energy operation of the virtual power plant based on the basic operation constraints of the virtual power plant and obtains the upper-level optimization scheduling instructions.

[0188] The lower-layer model predictive control module is configured to input the upper-layer optimization scheduling instructions into the lower-layer model predictive controller. The lower-layer model predictive controller comprehensively considers the deviation cost of the upper-layer reference value and the energy storage scheduling cost, and smoothes the source and load power fluctuations based on the basic operating constraints of the virtual power plant, obtaining the lower-layer operation results. The lower-layer operation results are fed back to the updated upper-layer model predictive control layer to optimize the iterative operation of the virtual power plant energy cycle. Embodiment 3 of the present invention provides a terminal including a processor and a storage medium; the storage medium is configured to store instructions; and the processor is configured to operate according to the instructions to execute the steps of the method.

[0189] Embodiment 4 of the present invention provides a computer-readable storage medium having a computer program stored thereon, which implements the steps of the method when executed by a processor.

[0190] Compared with the prior art, the beneficial effects of the present invention include at least:

[0191] The present invention takes energy storage degradation into consideration and improves the energy supply flexibility of rural solar virtual power plants under different operating conditions by aggregating flexible regulatory resources in rural areas. It also reduces the average degradation cost of energy storage batteries and the average operating cost of the system while ensuring that rural microgrids can safely and efficiently absorb new energy. This improves the new energy absorption capacity in rural areas and can effectively improve the economic benefits of production and life in rural areas.

[0192] The upper-level model objective function takes into account the energy storage degradation cost on the basis of the basic electricity purchase and sales cost and system operation and maintenance cost, and realizes the coordinated optimization of multiple cost factors, so that the virtual power plant can better control cost expenditure during operation, extend battery life, and improve the long-term economic benefits of the virtual power plant.

[0193] The dynamic energy storage degradation cost calculation model of the present invention clarifies the calculation method of the energy storage battery degradation cost in continuous time, dynamically estimates the battery degradation degree and corresponding cost, and improves the accuracy of battery degradation cost prediction.

[0194] The objective function of the lower-level model comprehensively considers the prediction error of the upper-level model (the deviation cost of the upper-level reference value) and the energy storage scheduling cost, so that the sum of the impact costs of prediction error fluctuations and load power changes on the system and the energy storage response costs to cope with power fluctuations is minimized, balancing the scheduling efficiency and the cost of energy storage, making the system more robust while minimizing the scheduling cost.

[0195] The present disclosure may be a system, method and / or computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for causing a processor to implement various aspects of the present disclosure.

[0196] A computer-readable storage medium can be a tangible device that can hold and store instructions for use by an instruction execution device. A computer-readable storage medium can be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device, such as a punch card or a raised structure in a groove on which instructions are stored, and any suitable combination thereof. As used herein, a computer-readable storage medium is not to be construed as a transient signal per se, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (e.g., a light pulse through a fiber optic cable), or an electrical signal transmitted through an electrical wire.

[0197] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to each computing / processing device, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network can include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. The network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions to be stored in the computer-readable storage medium in each computing / processing device.

[0198] The computer program instructions for performing the operations of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, and conventional procedural programming languages ​​such as "C" language or similar programming languages. Computer-readable program instructions may be executed entirely on a user's computer, partially on a user's computer, as an independent software package, partially on a user's computer, partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., utilizing an Internet service provider to connect via the Internet). In some embodiments, an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), may be personalized by utilizing the state information of the computer-readable program instructions. The electronic circuit may execute the computer-readable program instructions, thereby realizing various aspects of the present disclosure.

[0199] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered by the scope of protection of the claims of the present invention.

Claims

1. A method for optimizing the operation of a rural solar-biogas virtual power plant considering energy storage degradation, characterized by: include: Establish energy storage degradation cost models and basic operating constraints for virtual power plants; The upper-level model predictive control layer of the virtual power plant, based on the energy storage degradation cost model, considers dynamic energy storage degradation costs, electricity purchase and sales costs, and total operation and maintenance costs, optimizes the energy operation of the virtual power plant based on the basic operating constraints of the virtual power plant, and obtains upper-level optimization scheduling instructions; The upper-layer optimization scheduling instructions are input into the lower-layer model predictive controller. The lower-layer model predictive controller comprehensively considers the deviation cost of the upper-layer reference value and the energy storage scheduling cost, and smoothes the source and load power fluctuations based on the basic operating constraints of the virtual power plant. The lower-layer operation results are fed back to the updated upper-layer model predictive control layer to perform iterative operation optimization of the virtual power plant energy cycle; The deviation cost is: Where, is the deviation cost of the upper reference value, are the prediction deviations of biogas, energy storage, and grid exchange power, respectively. is the prediction deviation penalty coefficient of biogas, energy storage and grid exchange power.

2. The energy optimization operation method of a rural solar-biogas virtual power plant considering energy storage degradation according to claim 1 is characterized by: The energy storage degradation cost model is as follows: Where C BDC (t,d B (Δt)) is the degradation cost of the energy storage battery at time t; Δt is the time interval between discharge events; C B The replacement cost of energy storage batteries; P B (t) is the power of the energy storage battery at time t within Δt; L B (d B (Δt)) is the depth of charge and discharge d B Energy storage battery life at (Δt); d B (Δt) is the charge and discharge depth of the energy storage battery within Δt; E BA (t) is the actual capacity of the energy storage battery at time t; η Bc and η Bd are the charging and discharging efficiency coefficients of the energy storage battery, respectively.

3. The energy optimization operation method of a rural solar-biogas virtual power plant considering energy storage degradation according to claim 1 is characterized by: The basic operating constraints of the virtual power plant include the photovoltaic output constraints of the virtual power plant, the biogas gas turbine output constraints, the energy storage battery charging and discharging operation constraints, the energy storage battery charge state constraints, the power capacity constraints of the power grid, energy storage and biogas, and the power balance constraints.

4. The energy optimization operation method of a rural solar-biogas virtual power plant considering energy storage degradation according to claim 3 is characterized by: The photovoltaic output constraint of the virtual power plant is: 0≤P PV (t)≤P PVmax (2) Where, P PV (t) is the power of the photovoltaic power generation system of the virtual power plant at time t; P PVmax is the maximum power of the photovoltaic power generation system of the virtual power plant; The biogas gas turbine output constraint is: 0≤P Bi (t)≤P Bimax (3) Where, P Bi (t) represents the power of the biogas gas turbine of the virtual power plant at time t; P Bimax is the maximum power of the biogas gas turbine of the virtual power plant; The charge and discharge operation constraints of the energy storage battery are: Where, P Chr (t) is the charging power of the energy storage battery of the virtual power plant at time t; P Disc (t) is the discharge power of the energy storage battery of the virtual power plant at time t; is the maximum charge and discharge power of the energy storage battery; δ BS The charging and discharging efficiency of the energy storage battery; E BS (t) is the electromotive force of the energy storage battery; are the highest and lowest electromotive force of the energy storage battery respectively; The energy storage battery state of charge constraint is: Where, are the maximum and minimum charge factors of the energy storage battery respectively; t u ,t l are the time points of the upper and lower time domains, respectively; The power capacity constraints of the grid, energy storage and biogas are: Where, P M (t), P B (t) grid exchange and energy storage battery discharge respectively; The power balance constraint is: P L (t)=P M (t)+P B (t)+P PV (t)+P Bi (t),t∈Pt1,t u } (10) Where, P L (t), P M (t), P PV (t), P B (t), P Bi (t) are load, grid exchange, photovoltaic, energy storage battery discharge, and biogas power generation power, respectively.

5. The energy optimization operation method of a rural solar-biogas virtual power plant considering energy storage degradation according to claim 1 is characterized in that: The objective function of the upper model predictive control layer for optimizing the energy operation of the virtual power plant is: Where, F u Represents the objective function of the upper-layer model of the virtual power plant for optimizing the energy operation of the virtual power plant; Indicates t u The cost of electricity purchase and sale by the virtual power plant at the time; Indicates t u Total operation and maintenance costs of photovoltaic, biogas and energy storage at any given moment; Indicates t u Dynamic energy storage degradation cost at each moment; T u Indicates the prediction length of the upper model.

6. The energy optimization operation method of a rural solar-biogas virtual power plant considering energy storage degradation according to claim 5 is characterized by: The calculation formulas are: Where C buy (t u ) and P buy (t u ) are t u The electricity purchase price and power at the time; C sell (t u ) and P sell (t u ) are t u The electricity selling price and power at the time; r PV 、r Bi 、r B are the operation and maintenance cost coefficients of photovoltaic, biogas, and energy storage batteries respectively; P PV (t u ) is t u The power of the photovoltaic power generation system of the virtual power plant at each moment; P Bi (t u ) represents t u The power of the biogas gas turbine in the virtual power plant at any moment; P B (t u ) represents t u The power of the energy storage battery of the virtual power plant at any moment; g(t u ) is the state transition variable of the charge and discharge events in two consecutive time intervals; t u , t u -1 moment of energy storage battery degradation cost; E B (t u ), E B (t u -1) are t u , t u -1 moment the amount of power stored in the energy storage battery is reduced.

7. The energy optimization operation method of a rural solar-biogas virtual power plant considering energy storage degradation according to claim 6 is characterized by: g(t u ) is calculated as: Where, P B (t u -1) indicates t u -1 The power of the virtual power plant energy storage battery at time.

8. The energy optimization operation method of a rural solar-biogas virtual power plant considering energy storage degradation according to claim 1 is characterized by: The objective function of the lower-level model predictive controller for smoothing source-load power fluctuations is: Where, F l Predict the objective function of the controller for the underlying model; is the deviation cost of the upper reference value; The cost of energy storage dispatch; T l Indicates the prediction length of the underlying model.

9. The energy optimization operation method of a rural solar-biogas virtual power plant considering energy storage degradation according to claim 8 is characterized by: The calculation formulas are: Where, is the energy storage dispatch cost coefficient; Provide energy storage after dispatch; To provide energy storage before dispatch; Δt l is the time interval of the lower model.

10. The energy optimization operation method of a rural solar-biogas virtual power plant considering energy storage degradation according to claim 9 is characterized in that: The calculation formula is: Where, P Bi (t l ), P B (t l ), P M (t l ) are t l The output of biogas, energy storage and power grid at all times; are t obtained by upper layer optimization respectively. l Reference output values ​​of biogas, energy storage and power grid at all times.

11. A rural solar-biogas virtual power plant energy optimization operation system considering energy storage degradation, using the method according to any one of claims 1 to 10, characterized in that: The system comprises: Cost model and constraint building module, used to establish energy storage degradation cost model and basic operating constraints of virtual power plants; The upper-level model predictive control module is used for the upper-level model predictive control layer of the virtual power plant. Based on the energy storage degradation cost model, the upper-level model predictive control layer considers the dynamic energy storage degradation cost, the purchase and sale cost of electricity, and the total operation and maintenance cost. It optimizes the energy operation of the virtual power plant based on the basic operation constraints of the virtual power plant and obtains the upper-level optimization scheduling instructions. The lower-level model predictive control module is used to input the upper-level optimization scheduling instructions into the lower-level model predictive controller. The lower-level model predictive controller comprehensively considers the deviation cost of the upper-level reference value and the energy storage scheduling cost, and smoothes the source and load power fluctuations based on the basic operating constraints of the virtual power plant, obtains the lower-level operation results, and feeds back the lower-level operation results to the updated upper-level model predictive control layer to perform iterative operation optimization of the virtual power plant energy cycle.

12. A terminal comprising a processor and a storage medium; characterized in that: The storage medium is used to store instructions; The processor is configured to operate according to the instructions to execute the steps of the method according to any one of claims 1 to 10.

13. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the method according to any one of claims 1 to 10 are implemented.

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